Sensing capabilities of modern robotic and autonomous driving solutions constantly improve as technology advances and becomes more affordable [1]}.
Therefore, methods for visual place recognition span from classical approaches using RGB camera images to 360\(^\circ \)  range-based measuring systems, such as LiDARs and radars.
Appearance-based methods [2]}, [3]} leverage fine details of observed scenes, such as a texture of visible buildings. However, they fail under light-condition variance and seasonal changes.
Structure information-based place recognition methods address these limitations.
Modern 3D LiDARs, such as Velodyne HDL-64E, can capture up to 100 meters range providing a rich geometrical information about the observed scene.
LiDAR-based topological localization using learned descriptors is currently an active field of research, with a larger number of published methods [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}.
Nevertheless, high-end LiDARs are too expensive to be widely applicable, with the price as high as 70k USD per unit.
Additionally, LiDAR readings are adversely affected by extreme environmental conditions such as fog, heavy rain or snow [13]}.
In such challenging conditions, radars show a great potential as they are more robust against atmospheric phenomena.
Moreover, modern frequency-modulated continuous wave (FMCW) scanning radars cover broader area, having an operating range up to 200 meters.
However, radar-based localization is relatively little exploited [13]}, [15]}.
This can be attributed to limited availability of sufficiently large and diverse datasets.
The situation has improved recently with the release of large-scale MulRan [13]} and Radar RobotCar [17]} datasets.
